Sr Applied Data Scientist/Engineer, Decision Intelligence

Workwave; Brierley Hill; On-site; GBP 70,000 - 100,000; Full time

Job summary

Workwave seeks a product-minded applied data scientist or engineer to turn raw operational data into products that help customers decide and run their businesses more effectively.

This is not purely research or internal analytics; we want an owner who frames problems, learns the domain, builds data when missing, ships the model, and stays with it until customers act on it and measure the reward.

Responsibilities

• Own the Outcome: take an ambiguous customer problem, decide whether ML is the right answer, build it, and stay with it until customers are acting on it.
• Learn the Domain: get fluent in the semantics of how our customers operate—what a route, a crew, or a service history actually means.
• Build the Data You Need: when features don’t exist, create them in Snowflake and dbt rather than waiting for someone else to.
• Make the Value Legible: decide how a prediction reaches the customer so they understand it, trust it, and act on it — then report impact back in numbers.
• Measure Honestly: define offline and online evaluation for model quality, drift, and reliability, and design tests that prove a feature improved outcomes

Tools

Snowflake

dbt

Job description

We are looking for a product-minded applied data scientist or engineer to turn raw operational data into products that measurably improve how our customers make decisions and run their businesses. Which label you carry matters less to us than whether customers end up better off.

This is not a research-only role, nor a service-oriented internal analytics position—and it is not a role for a model builder alone. We want an owner: someone who frames the problem, learns the domain, builds the data when it doesn't exist, ships the model, and stays with it until customers are acting on it and can measure the reward. The road runs through data engineering; deployment and testing are part of delivery, not a handoff. You understand that great models are not just accurate in notebooks—they are usable, explainable, and measurable inside a real product.

Whether you came to this work through statistics, software, analytics, or data engineering, you have a strong bias toward shipping.

From Raw Data to Better Decisions

• Own the Outcome: Take an ambiguous customer problem, decide whether ML is even the right answer, build it, and stay with it until customers are acting on it.

• Learn the Domain: Get fluent in the semantics of how our customers operate—what a route, a crew, or a service history actually means. A model that is accurate but wrong about the domain creates nothing.

• Build the Data You Need: When the features don't exist, create them in Snowflake and dbt rather than waiting for someone else to.

Prove It and Make It Felt

• Make the Value Legible: Decide how a prediction reaches the customer so they understand it, trust it, and act on it—then report realized impact back to Product and the business in numbers that hold up.

• Measure Honestly: Define offline and online evaluation for model quality, drift, and reliability, and design the A/B tests or causal analyses that prove a feature improved customer outcomes